fix test

thunder/dynamo/compiler.py

@@ -85,7 +85,8 @@ def __call__(self, gm: torch.fx.GraphModule, sample_args: list[torch.SymInt, tor
         return split_module
 
     def save_reproducer_to_folder(self, reproducer_folder: str | PathLike):
-        """Save the reproducer script for the GraphModule executed by Thunder to the specified `reproducer_folder`.
+        """
+        Save the reproducer script for the GraphModule executed by Thunder to the specified `reproducer_folder`.
         Each saved script is named as "g[graph_id]_thunder_[module_id]", where:
             - `graph_id` indexes the graph generated by Dynamo, which is then passed to Thunder.
             - `module_id` indexes the submodule split by the :func:`thunder.dynamo.utils._splitter`.

thunder/dynamo/utils.py

@@ -495,14 +495,19 @@ def _get_example_inputs_from_placeholder(
                 return _get_example_input_tensor_metadata(ev)
             return ev.detach().clone().requires_grad_(ev.requires_grad)
 
-    check("example_value" in node.meta, lambda: "example_value does not exist in the meta of {node}", ValueError)
+    check("example_value" in node.meta, lambda: f"example_value does not exist in the meta of {node}", ValueError)
     example_value = node.meta["example_value"]
 
     if isinstance(example_value, torch.Tensor):
         ev_metadata = _get_example_input_tensor_metadata(example_value)
         if only_metadata:
             return ev_metadata
         return _create_random_tensor_from_tensor_metadata(ev_metadata)
+    elif isinstance(example_value, tuple):
+        ev_metadatas = tuple(_get_example_input_tensor_metadata(e_v) for e_v in example_value)
+        if only_metadata:
+            return ev_metadatas
+        return tuple(_create_random_tensor_from_tensor_metadata(ev_metadata) for ev_metadata in ev_metadatas)
     elif isinstance(example_value, torch.types.py_sym_types):
         return example_value.node.hint
     else: